VLA Pre-training & World Models | Humanoid

hirehire; Greater London; On-site; GBP 90,000 - 135,000; Full time

Benefits offered by this job

Equity

30+ days off

Private healthcare

Pension

Free meals in office

Visa sponsorship & relocation

Job summary

Humanoid in London is seeking a senior VLA Pre-training Engineer / Deep Learning Engineer to join two tracks: VLA Pre-training and World Models. On-site at King’s Cross, visa sponsorship and relocation to London offered.

The role covers pre-training on diverse trajectory data, policy training with cloning/RL, scalable data pipelines, and evaluation tools for robot deployment in real time.

Qualifications

• 3+ years building deep learning systems in industry or research.
• Hands-on experience with at least one: LLMs, VLMs, image or video generative models (architecture, training, inference).
• Trained models yourself: pre-training runs, distributed infrastructure, APIs, fine-tuning not enough.
• Experience with deep learning infrastructure: streaming datasets, checkpointing, state management, distributed training strategies.
• Strong Python and PyTorch or JAX; able to profile, optimize data loaders and write maintainable research code.
• Strong coding fundamentals; LeetCode-style coding round.
• Empirical rigour: clear baselines, honest evaluations, precise experiment docs.
• Proactive; able to tackle open, messy problems without a spec.
• Working English.

Responsibilities

• Pre-train base models on a diverse multi-embodiment corpus of trajectories and synthetic data.
• Post-train policies with behaviour cloning and RL; own data-to-deployment loop.
• Collaborate with Data Collection: specify good data, identify failure modes, ensure coverage.
• Run pre-, mid- and post-training on the VLA stack; explore new modalities and architecture changes.
• Build and maintain continuous pipelines: ingest synthetic data, version datasets, surface failures for retraining.
• Scale distributed training and optimize models for real-time edge inference on robots with MLOps/data platform teams.

Skills

Deep learning systems

LLMs / VLMs / video models

Python & PyTorch / JAX

DL infra & distributed training

English proficiency

Tools

PyTorch

JAX

Isaac Sim

Job description

Role: VLA Pre-training Engineer / Deep Learning Engineer – World Models (2 openings)
Company: Humanoid
Location: London, UK (King’s Cross) – on-site, five days a week
Job Type: Full-time

About Humanoid

Humanoid is building the world’s most capable, commercially scalable and safe humanoid robots. Its platform, HMND-01, is already deployed in real industrial environments, which makes Humanoid the most serious humanoid robotics company in Europe.

• 300+ people, 34+ nationalities, flat structure: your decisions ship directly into real robots.

• Offices in London (HQ, King’s Cross), Boston, Vancouver and San Diego.

• Values: Speed, Ambition, Passion, Trust.

About the role

The VLA Core team builds the base vision-language-action model that the entire manipulation pipeline depends on. This is deep learning at scale: training models from scratch on multi-embodiment trajectory corpora and internet-scale video, with real deployment constraints. The model has to fit and run on a real robot.

Robotics experience is not required. If you come from LLMs, VLMs or video generation, be ready to get up to speed on a new domain quickly.

This is a senior individual contributor role with no management track. The team is research-adjacent, but the output is models that run on robots. Results are shared as blog posts, not papers.

There are two tracks in one team. They are the same role with a different focus, so you can lean into either.

Track 1: VLA Pre-training

• Pre-train base models on a diverse multi-embodiment corpus of trajectories, fine-tune policies to excel at specific tasks and explore synthetic data.

• Post-train policies with behaviour cloning and RL, and own the full loop from data to deployment.

• Partner with the Data Collection team: specify what good data looks like, identify failure modes, ensure diversity and coverage.

• Run pre-, mid- and post-training on the VLA stack; explore new modalities and architecture changes.

• Build and maintain continuous pipelines: ingest synthetic data and teleop logs, version them, apply weak-supervision labelling, curate balanced datasets and automatically surface fresh failure cases into retraining.

• Work with the MLOps and Data Platform teams to scale distributed training and optimize models for real-time edge inference on the robot.

Track 2: World Models

• Design and train multimodal, action-conditioned world models (video, state, action and language) using diffusion-based and transformer architectures.

• Build video prediction and dynamics models that stay physically consistent over long horizons, including contact-rich manipulation, and serve as pretrained priors for VLA policies.

• Develop learned-simulator evaluation: score candidate policies offline, predict real-world success rates before deployment, roll out policy futures for safety review and planning.

• Generate synthetic rollouts and counterfactual experience (rare events, cross-platform and sim-to-real transfer) and measure their effect on downstream task performance.

• Build data pipelines that turn fleet telemetry, teleoperation logs and internet-scale video into training corpora.

• Run scaling and ablation studies on architecture, data mixture and context length.

What you need to succeed

• 3+ years building deep learning systems in industry or research, with shipped models or published work to show for it.

• Deep hands-on experience with at least one of: LLMs, VLMs, image or video generative models (architecture, training and inference). For the World Models track: diffusion models, autoregressive transformers, latent-variable models or video prediction.

• You have actually trained models yourself: pre-training runs, distributed infrastructure, the works. Connecting APIs, building agents or a quick fine-tune is not enough.

• Experience with deep learning infrastructure: streaming datasets, checkpointing and state management, distributed training strategies, debugging numerics and training instabilities.

• Strong Python and PyTorch or JAX. You can profile, optimize data loaders and write maintainable research code.

• Strong coding fundamentals. The process includes a LeetCode-style coding round, so come prepared.

• Empirical rigidity: careful evaluations, honest baselines, clear experiment docs, crisp communication of trade-offs.

• You are proactive: you can take an open, messy problem and drive it without waiting for a spec.

• Working English.

Nice to have

• Robotics or autonomous driving experience; familiarity with simulators (Isaac Sim, MuJoCo) and sim-to-real.

• Experience applying RL to LLMs or robotics.

• Experience with VLA models; familiarity with OpenVLA, Physical Intelligence (π) models or similar open-source frameworks.

• Experience with world models for robotics or autonomous driving (action-conditioned video models, learned simulators, model-based RL).

• Proven productization of deep nets: latency and throughput constraints, on-device optimization, fast inference for generative models.

• Publications at top-tier DL conferences (NeurIPS, ICML, ICLR, CoRL, CVPR) or equivalent open-source contributions.

Interview process

• Intro call with Igor, hirehire’s AI researcher.

• HR screen with Humanoid.

• Coding round (LeetCode-style).

• Specialization interview (pre-training or world models).

• Technical execution interview: how you work, collaborate and drive problems.

• Optional chat with a tech leader.

What you get

• Salary: a wide range, set by the level you show in the interviews.

• Equity: stock options with meaningful upside as Humanoid scales.

• 30+ paid days off: 23 days annual leave, all UK bank holidays and a company shutdown between Christmas and New Year.

• Private healthcare: fully funded, including virtual and in-person care.

• Pension: 8% total contribution (5% employee, 3% employer) on full earnings.

• Daily food: free breakfast, catered lunch and snacks in the office.

• Real ownership: direct access to founding leadership, real input on product direction and room to drive key initiatives from day one.

• Visa sponsorship and relocation to London.